1. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Werner CEUSTERS, MD Director, Ontology Research Group Center of Excellence in Bioinformatics and Life Sciences University at Buffalo, NY, USA Meeting Meaningful Use Criteria through Referent Tracking WORKSHOP 9: EHRs/EMRs Utilization of EHRs/EMRs to Further Drug and Disease Related Research Tuesday, April 12, 2011 8:00-11:30 AM, Boston, MA

2. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Short personal history 1959 - 2011 1977 1989 1992 1998 2002 2004 2006 1993 1995

3. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Benefits of Electronic Health Records (EHRs) for providers and their patients: –Complete and accurate information, shared, coordinated, –Better access to information, when and where needed, –Patient empowerment, proactive, consent. ONCHIT: http://healthit.hhs.gov

4. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ONCHIT’s Legislation and Regulations The Health Information Technology for Economic and Clinical Health (HITECH) Act allows HHS to promote health information technology (HIT) to improve health care quality, safety, and efficiency. Results : –Incentive Program for EHRs issued by CMS: Stage I requirements for certified EHR technology in order to qualify for the payments: ‘Meaningful Use’ – 2011-2012; –Standards and Certification Criteria for EHRs; –Request for Comment - Stage 2 Definition of Meaningful Use in 2013 - 2014. ONCHIT: http://healthit.hhs.gov

5. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Examples of Meaningful Use (MU) criteria CPOE for Medication orders, Drug-drug/drug-allergy interaction checks, Record demographics, Report Quality Committee Measures, Maintain active medication list, Maintain active medication allergy list, Record vital signs, Record smoking status.

6. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Examples of imposed standards Patient summary record: –HL7 CDA R2 (CCD) or ASTM E2369 (CCR). Problem list: –ICD-9-CM or SNOMED CT®. Procedures: –ICD-9-CM or HCPCS + CPT-4. Laboratory orders and results: –LOINC®.

7. New York State Center of Excellence in Bioinformatics & Life Sciences R T U MU and Drug/Disease related research Interesting MU requirements: –coded … problem list of diagnoses, drug-drug and drug-allergy interaction checks, –medication list, –medication allergies, vital signs: height, weight, blood pressure, laboratory test results, demographic data: preferred language, insurance type, gender, race, ethnicity, date of birth, and date and cause of death in the event of mortality.

8. New York State Center of Excellence in Bioinformatics & Life Sciences R T U MU criteria and certified EHRs have fallacies 1.Crippled idea about ‘problem list of diagnoses’

9. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Crippled idea about ‘problem list of diagnoses’ Basis of Problem List: –Larry Weed’s Problem Oriented Medical Record Each medical record should have a complete list of all the patient's problems, including both clearly established diagnoses and all other unexplained findings that are not yet clear manifestations of a specific diagnosis. Includes: –diagnosis− physical finding –lab abnormality− physiologic finding –social issue− symptom –demographic issue Weed LL. Medical records that guide and teach. N Engl J Med. 1968 Mar 14;278(11):593-600.

10. New York State Center of Excellence in Bioinformatics & Life Sciences R T U MU criteria and certified EHRs have fallacies 1.Crippled idea about ‘problem list of diagnoses’ 2.Conflation of diagnosis and disease/disorder

11. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Conflation of diagnosis and disease/disorder The disorder is thereThe diagnosis is here The disease is there

12. New York State Center of Excellence in Bioinformatics & Life Sciences R T U MU criteria and certified EHRs have fallacies 1.Crippled idea about ‘problem list of diagnoses’ 2.Conflation of diagnosis and disease/disorder 3.The structure of EHR data (information model) is not close enough to the structure of that what the data are about

13. New York State Center of Excellence in Bioinformatics & Life Sciences R T U EHR Information Models (simplified) patient diagnosis drug finding encounterpatient diagnosis drug finding

14. New York State Center of Excellence in Bioinformatics & Life Sciences R T U What is on the side of the patient Scheuermann R, Ceusters W, Smith B. Toward an Ontological Treatment of Disease and Diagnosis. 2009 AMIA Summit on Translational Bioinformatics, San Francisco, California, March 15-17, 2009;: 116-120. Omnipress ISBN:0-9647743-7-2

15. New York State Center of Excellence in Bioinformatics & Life Sciences R T U MU criteria and certified EHRs have fallacies 1.Crippled idea about ‘problem list of diagnoses’ 2.Conflation of diagnosis and disease/disorder 3.The structure of EHR data (information model) is not close enough to the structure of that what the data are about 4.Unjustified belief that the use of unambiguous codes renders EHR data unambiguous

16. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Using generic representations for specific entities is inadequate 557204/07/199026442006closed fracture of shaft of femur 557204/07/199081134009Fracture, closed, spiral 557212/07/199026442006closed fracture of shaft of femur 557212/07/19909001224Accident in public building (supermarket) 557204/07/199079001Essential hypertension 093924/12/1991255174002benign polyp of biliary tract 230921/03/199226442006closed fracture of shaft of femur 230921/03/19929001224Accident in public building (supermarket) 4780403/04/199358298795Other lesion on other specified region 557217/05/199379001Essential hypertension 29822/08/19932909872Closed fracture of radial head 29822/08/19939001224Accident in public building (supermarket) 557201/04/199726442006closed fracture of shaft of femur 557201/04/199779001Essential hypertension PtIDDateSNOMED CT codeNarrative 093920/12/1998255087006malignant polyp of biliary tract

17. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A solution: Referent Tracking A paradigm under development since 2005, –designed to keep track of relevant portions of reality and what is believed and communicated about them,

18. New York State Center of Excellence in Bioinformatics & Life Sciences R T U 18

19. New York State Center of Excellence in Bioinformatics & Life Sciences R T U L1 L2 L3 19

20. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A solution: Referent Tracking A paradigm under development since 2005, –designed to keep track of relevant portions of reality and what is believed and communicated about them, –enabling adequate use of realism-based ontologies, terminologies, thesauri, and vocabularies,

21. New York State Center of Excellence in Bioinformatics & Life Sciences R T U OBO Foundry L1 L2 L3 21

22. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A solution: Referent Tracking A paradigm under development since 2005, –designed to keep track of relevant portions of reality and what is believed and communicated about them, –enabling adequate use of realism-based ontologies, terminologies, thesauri, and vocabularies, –originally conceived to track particulars on the side of the patient and his environment denoted in his EHR,

23. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Between ‘generic’ and ‘specific’ L1. First-order reality L2. Beliefs (knowledge) GenericSpecific DIAGNOSIS INDICATION my doctor’s work plan my doctor’s diagnosis MOLECULE PERSON DISEASE PATHOLOGICAL STRUCTURE PORTION OF INSULIN DRUG me my blood glucose level my NIDDM my doctor my doctor’s computer L3. Representation ‘person’‘drug’‘insulin’‘W. Ceusters’‘my sugar’ Referent TrackingBasic Formal Ontology GenericSpecific 23

24. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A solution: Referent Tracking A paradigm under development since 2005, –designed to keep track of relevant portions of reality and what is believed and communicated about them, –enabling adequate use of realism-based ontologies, terminologies, thesauri, and vocabularies, –originally conceived to track particulars on the side of the patient and his environment denoted in his EHR, –but since then studied in and applied to a variety of domains, –and now evolving towards tracking absolutely everything.

25. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The problem in a nutshell Generic terms used to denote specific entities do not have enough referential capacity –Usually enough to convey that some specific entity is denoted, –Not enough to be clear about which one in particular. For many ‘important’ entities, unique identifiers are used: –UPS parcels –Patients in hospitals –VINs on cars –…

26. New York State Center of Excellence in Bioinformatics & Life Sciences R T U explicit reference to the concrete individual entities relevant to the accurate description of some portion of reality,... Fundamental goals of ‘our’ Referent Tracking Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78.

27. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Method: numbers instead of words Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78. –Introduce an Instance Unique Identifier (IUI) for each relevant particular (individual) entity 78

28. New York State Center of Excellence in Bioinformatics & Life Sciences R T U 557204/07/199026442006closed fracture of shaft of femur 557204/07/199081134009Fracture, closed, spiral 557212/07/199026442006closed fracture of shaft of femur 557212/07/19909001224Accident in public building (supermarket) 557204/07/199079001Essential hypertension 093924/12/1991255174002benign polyp of biliary tract 230921/03/199226442006closed fracture of shaft of femur 230921/03/19929001224Accident in public building (supermarket) 4780403/04/199358298795Other lesion on other specified region 557217/05/199379001Essential hypertension 29822/08/19932909872Closed fracture of radial head 29822/08/19939001224Accident in public building (supermarket) 557201/04/199726442006closed fracture of shaft of femur 557201/04/199779001Essential hypertension PtIDDateSNOMED CT CodeNarrative 093920/12/1998255087006malignant polyp of biliary tract IUI-001 IUI-003 IUI-004 IUI-005 IUI-007 IUI-002 IUI-012 IUI-006 7 distinct disorders Codes for ‘types’ AND identifiers for instances

29. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Stead and Lin’s ‘Principles for Success’ in Health IT Evolutionary change Radical change: Principle 6: Architect Information and Workflow Systems to Accommodate Disruptive Change »Organizations should architect health care IT for flexibility to support disruptive change rather than to optimize today’s ideas about health care. Principle 7: Archive Data for Subsequent Re-interpretation »Vendors of health care IT should provide the capability of recording any data collected in their measured, uninterpreted, original form, archiving them as long as possible to enable subsequent retrospective views and analyses of those data. NOTE NOTE: ‘See, for example, Werner Ceusters and Barry Smith, “Strategies for Referent Tracking in Electronic Health Records” Journal of Biomedical Informatics 39(3):362-378, June 2006.’ Willam W. Stead and Herbert S. Lin, editors; Committee on Engaging the Computer Science Research Community in Health Care Informatics; National Research Council. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions (2009)

30. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The shift envisioned From: –‘this human being is a 40 year old patient with a stomach tumor’ To (something like) : –‘this-1 on which depends this-2 and this-3 has this-4’, where this-1 instanceOf human being at t 1 this-2 instanceOf age-of-40-years at t 2 this-2 qualityOf this-1 at t 2 this-3 instanceOf patient-roleat t 3 this-3 roleOf this-1at t 3 this-4 instanceOf tumorat t 4 this-4 partOf this-5at t 6 this-5 instanceOf stomachat t 7 this-5 partOf this-1at t 8 …

31. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The shift envisioned From: –‘this human being is a 40 year old patient with a stomach tumor’ To (something like) : –‘this-1 on which depends this-2 and this-3 has this-4’, where this-1 instanceOf human being at t 1 this-2 instanceOf age-of-40-years at t 2 this-2 qualityOf this-1 at t 2 this-3 instanceOf patient-roleat t 3 this-3 roleOf this-1at t 3 this-4 instanceOf tumorat t 4 this-4 partOf this-5at t 6 this-5 instanceOf stomachat t 7 this-5 partOf this-1at t 8 … denotators for particulars (specific entities)

32. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The shift envisioned From: –‘this human being is a 40 year old patient with a stomach tumor’ To (something like) : –‘this-1 on which depends this-2 and this-3 has this-4’, where this-1 instanceOf human being at t 1 this-2 instanceOf age-of-40-years at t 2 this-2 qualityOf this-1 at t 2 this-3 instanceOf patient-roleat t 3 this-3 roleOf this-1at t 3 this-4 instanceOf tumorat t 4 this-4 partOf this-5at t 6 this-5 instanceOf stomachat t 7 this-5 partOf this-1at t 8 … denotators for appropriate relations

33. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The shift envisioned From: –‘this human being is a 40 year old patient with a stomach tumor’ To (something like) : –‘this-1 on which depends this-2 and this-3 has this-4’, where this-1 instanceOf human being at t 1 this-2 instanceOf age-of-40-years at t 2 this-2 qualityOf this-1 at t 2 this-3 instanceOf patient-roleat t 3 this-3 roleOf this-1at t 3 this-4 instanceOf tumorat t 4 this-4 partOf this-5at t 6 this-5 instanceOf stomachat t 7 this-5 partOf this-1at t 8 … denotators for universals or classes (what is generic) or particulars

34. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The shift envisioned From: –‘this human being is a 40 year old patient with a stomach tumor’ To (something like) : –‘this-1 on which depends this-2 and this-3 has this-4’, where this-1 instanceOf human being at t 1 this-2 instanceOf age-of-40-years at t 2 this-2 qualityOf this-1 at t 2 this-3 instanceOf patient-roleat t 3 this-3 roleOf this-1at t 3 this-4 instanceOf tumorat t 4 this-4 partOf this-5at t 6 this-5 instanceOf stomachat t 7 this-5 partOf this-1at t 8 … time periods (for continuants) when the relationships hold

35. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Relevance: the way RT-representations interact with representations of generic portions of reality instance-of at t #105 caused by

36. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Referent Tracking based data warehousing

37. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Networks of Referent Tracking systems

38. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Unique identifier for: –each data-element and combinations thereof (L3), –what the data-element is about (L1), –each generated copy of an existing data-element (L3), –each transaction involving data-elements (L1); Identifiers centrally managed in RTS; Exclusive use of ontologies for type descriptions following OBO-Foundry principles; Centrally managed data dictionaries, data-ownership, exchange criteria. General principles of RT-enabled data warehousing (1)

39. New York State Center of Excellence in Bioinformatics & Life Sciences R T U General principles of RT-enabled data warehousing (2) Central inventory of ‘attributes’ but peripheral maintenance of ‘values’; Identifiers function as pseudonyms: –centrally known that for person IUI-1 there are values about instances of UUI-2 maintained by researcher/clinician IUI-3 for periods IUI-4, IUI-5, … Disclosure of what the identifiers stand for based on need and right to know; Generation of off-line datasets for research with transaction-specific identifiers for each element.

40. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Feedback to clinical care Finding ‘similar’ patient cases: –suggestions for prevention, investigation, treatment; ‘Outbreak’ detection; Comparing outcomes; –related to disorders, providers, treatments, … Links to literature; Clinical trial selection; …